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For the issue of traditional unmanned aerial vehicle (UAV) local path planning algorithms easily falling into local optima, an algorithm for local path planning that combines deep reinforcement learning and memory-guided strategy is proposed. The algorithm takes the UAV heading angle as the learning action, and detection information as the state. It integrates the Deep Q-network (DQN) algorithm to learn path planning strategies. When the UAV gets trapped in a local optimum, the memory-guided strategy utilizes historical detection information to guide the UAV out of the local optimum, completing local path planning for UAVs in unknown environments. Simulation experiments verify that the proposed algorithm achieves higher success rates and shorter average flight distances.

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Deep Reinforcement Learning-Based Local Path Planning with Memory-Guided

  • 会议论文

  • 发表日期：

    2025年

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  • Xu Wang,
  • Xiaobin Xu,
  • Shiyao Lin,
  • Junfang Fan,
  • Zhihao Gao,
  • Chengyu Gu

摘要

For the issue of traditional unmanned aerial vehicle (UAV) local path planning algorithms easily falling into local optima, an algorithm for local path planning that combines deep reinforcement learning and memory-guided strategy is proposed. The algorithm takes the UAV heading angle as the learning action, and detection information as the state. It integrates the Deep Q-network (DQN) algorithm to learn path planning strategies. When the UAV gets trapped in a local optimum, the memory-guided strategy utilizes historical detection information to guide the UAV out of the local optimum, completing local path planning for UAVs in unknown environments. Simulation experiments verify that the proposed algorithm achieves higher success rates and shorter average flight distances.